The tremendous growth of the wireless and mobile user population, coupled with the bandwidth requirements of multimedia applications, calls for efficient use of the scarce radio spectrum allocated to wireless and mobile communications. The deployment of efficient modulation schemes, such as quadrature amplitude modulation, or QAM, which uses less bandwidth than other types of modulation such as FM or AM, is therefore a common practice in modern communication systems.
QAM is well known in the communications art and combines characteristics of both phase modulation and amplitude modulation to reduce the bandwidth required to carry a certain amount of information in an information-bearing signal. In QAM, information is conveyed using changes in both the amplitude of a carrier wave and the relative phase angle of the carrier signal with respect to a reference angle. Using QAM modulation to convey digital data, 2, 3, 4, or more, bits of digital information can be conveyed per QAM signal element.
Multi-carrier QAM is a technique in which an information-bearing signal, such as serial digitized voice or digital data from a computer or other machine for example, is divided up into multiple, separate, frequency division multiplexed QAM signals. Each QAM signal occupies a discrete frequency band (with each of the bands being substantially frequency adjacent to the others) and carries a portion of the information in the information-bearing signal. Multi-carrier QAM may further support several channel bandwidths. One well-known form of multi-carrier QAM is Orthogonal Frequency Division Multiplexing (OFDM). A chief advantage of multi-carrier techniques is their improved performance in time-dispersive channels relative to single-carrier methods.
FIG. 1 illustrates a representation of a transmit energy spectrum, i.e., a radio frequency (“RF”) signal, output from a transmitter in a multi-carrier QAM based communications system. The frequency spectrum output is shown as having eight sub-channels 12, 14, 16, 18, 20, 22, 24 and 26 centered about a center frequency f0. Moreover, each sub-channel 12, 14, 16, 18, 20, 22, 24 and 26 has its own center frequency f0+f1, f0+f2, f0+f3, f0+f4, f0+f5, f0+f6, f0+f7, and f0+f8 respectively (i.e., 32, 34, 36, 38, 40, 42, 44 and 46 respectively). While FIG. 1 illustrates a transmit energy spectrum having eight sub-channels, it is appreciated by those skilled in the art that the frequency spectrum may have more or fewer sub-channels, for instance 4, 16 or 24 sub-channels and that the number of sub-channels typically corresponds to a given channel bandwidth. Moreover, it is further understood that the number of sub-channels may be an even number of sub-channels as illustrated in FIG. 1 or may, alternatively, be an odd number of sub-channels.
FIG. 2 illustrates an exemplary slot format 200 comprising information that might be present on each of the eight sub-channels shown in FIG. 1. In slot format 200, the horizontal dimension is indicative of time, and the vertical dimension of frequency. Each of the signals 12, 14, 16, 18, 20, 22, 24 and 26 at frequencies f1, f2, f3, f4, f5, f6, f7, and f8, respectively, includes non-data symbols (pilot symbols 208 and synchronization symbols 210) and data symbols 212. Each of the non-data symbols 208, 210 are disposed among the data symbols 212 at a plurality of pre-designated locations/times that are known to an intended receiver in the multi-carrier QAM based communications system and can therefore be used for timing synchronization and frequency synchronization, as well as channel estimation, by the receiver. The data symbols 212 constitute the information to be communicated via the transmission.
FIG. 3 illustrates exemplary phase and amplitude mappings of each of the symbol types present in the slot format of FIG. 2. Map 310 is a phase and amplitude mapping for a synchronization symbol 210, and map 320 is a phase and amplitude mapping for a pilot symbol 208. Synchronization symbol mappings and pilot symbol mappings can be arbitrarily chosen, though typically they have identical amplitudes as illustrated by the circle loci shown in maps 310 and 320. All that is necessary is that their phase and amplitude values, in addition to their locations, be predetermined and known to the receiver. Map 330 is a phase and amplitude mapping for a data symbol 212. This exemplary data symbol map represents a 16QAM constellation capable of conveying 4 bits of information per symbol. Since the data symbols 212 represent four-bit binary words, there are sixteen (16) possible different vectors or symbols that correspond to the 16 different binary values. Every possible bit pattern is assigned a location on the coordinate system shown in map 330. A complex vector, having a magnitude corresponding to its length, and a phase angle corresponding to its angular displacement from a reference axis, represent the bits that it “points” to at a particular constellation point.
A typical receiver in a multi-carrier QAM based communications system comprises conventional apparatus (e.g., a digital signal processor that includes a demodulator portion) for performing a timing synchronization method for detecting the presence of the synchronization symbols 210 and determining from these symbols the proper sampling timing for one or more sampling devices in the receiver and the times at which each of the pilot, synchronization and data symbols 208, 210 and 212 in a given slot 200 will arrive. In this conventional timing synchronization methodology or algorithm (also referred to herein as “coarse timing synchronization”), a comparison, or correlation is made between the received digital signal samples and the known waveform produced by the synchronization symbols 210. The timing corresponding to the best match between the received signal and the known waveform determines the relative timing of the synchronization symbols. From this, a coarse timing estimate is derived for use in demodulation and bit detection for that slot. If timing estimates for multiple slots are available, these may be further averaged to improve the timing estimate.
A typical receiver in a multi-carrier QAM based communications system further comprises conventional apparatus (e.g., a digital signal processor that includes a demodulator portion) for performing an automatic frequency correction (AFC) methodology for determining and correcting an offset of the center frequency f0. An offset between the expected and actual center frequency of a received signal can occur due to such factors as oscillator drift or Doppler shift caused by relative movement between transmitter and receiver. This offset can degrade system performance, as will be explained shortly.
A conventional frequency correction methodology or algorithm (also referred to herein as “coarse frequency correction”) makes use of complex channel gain estimates derived from synchronization and pilot samples near the beginning of the slot. For each sub-channel, the channel phase differences between adjacent synchronization and pilot sample pairs are calculated, which, coupled with the corresponding time differences, provide frequency error information. A weighted average of the frequency error values, over several sample pairs and sub-channels, defines a frequency error estimate for that slot. If frequency error estimates for multiple slots are available, these may be further averaged to improve the frequency estimate.
The conventional timing synchronization methodology and frequency correction methodology may be adequate in situations where long-term averaging of time and frequency estimates over multiple slots is possible. However, the accuracy of conventionally derived timing and frequency estimates may be inadequate if the transmitter transmits only a single isolated slot (for example, a random access request), or only a few slots. Taking timing synchronization as an example, synchronization symbols typically occupy only a short portion of each slot, and the timing information that is conventionally derived therefrom may not be sufficiently accurate, or adequately representative, of the composite timing as experienced over the slot's entire duration. Moreover, even if long-term averaging is possible, conventional timing and frequency estimators typically converge to the centroids of respective delay and Doppler profiles of what may be a multi-path fading channel. This may lead to degraded channel estimation performance for non-symmetric delay and Doppler profiles.
In addition, a typical receiver in a multi-carrier QAM based communications system employs pilot-symbol assisted channel amplitude and phase (complex gain) estimation at the data symbol 212 locations. This is accomplished by first calculating the complex channel gains at the known pilot and synchronization symbol 208 and 210 locations, as previously mentioned, for reference. A filtering or interpolation operation is then applied to these complex channel gains to estimate the complex channel gain at each data symbol 212 location. These complex channel gain estimates are then applied to undo the effect of the channel on the transmitted data symbols. The channel estimation may be carried out using one-dimensional filtering (for example, via a set of channel estimation filters with a separate time-domain filtering operations for each sub-channel, and using only the pilot and synchronization symbols on that sub-channel), or using two-dimensional filtering, where the set of pilot/synchronization symbols utilized for the channel estimate for each sub-channel spans multiple sub-channels and symbol times.
It is further known by those of ordinary skill in the art that selecting from multiple channel estimation filters, with Doppler and/or delay designs carefully selected to match the expected range of channel conditions, can improve performance (e.g., receiver sensitivity) by minimizing estimation error. The key to this adaptive methodology is accurate classification of the channel, which can be compromised due to residual timing and/or frequency offsets remaining after coarse timing synchronization and frequency corrections are applied. These residual offsets result in a shifting (or un-centering) of the multi-path channel response (or the delay/Doppler profile thereof) relative to the channel estimation filter bandwidths and can result in wider than actual channel classification. This, in turn, can result in the selection of a wider than necessary channel estimation filter design, which nullifies the sensitivity improvement that could be achieved with a narrower design. Worse yet, these residual timing and frequency errors can result in cases where even the widest available channel estimation filter (or, in the case of a system not employing a multiple filter sensitivity improvement methodology, the only available channel estimation filter) is not wide enough. The result here can be more severe than a mere surrender of sensitivity improvement, as data recovery may not be possible.
Thus, there exists a need for a method and apparatus for performing timing and frequency error estimation and correction to determine and remove residual errors remaining after the coarse frequency and timing corrections are made. It is further desirable that this method and apparatus be compatible with and enhance the performance of a system that utilizes an adaptive channel estimation scheme.